A general prediction model for seven heavy metals was established using the heavy metal contents of 207soil samples measured by a portable X-ray fluorescence spectrometer(XRF)and six environmental factors as model cor...A general prediction model for seven heavy metals was established using the heavy metal contents of 207soil samples measured by a portable X-ray fluorescence spectrometer(XRF)and six environmental factors as model correction coefficients.The eXtreme Gradient Boosting(XGBoost)model was used to fit the relationship between the content of heavy metals and environment characteristics to evaluate the soil ecological risk of the smelting site.The results demonstrated that the generalized prediction model developed for Pb,Cd,and As was highly accurate with fitted coefficients(R^(2))values of 0.911,0.950,and 0.835,respectively.Topsoil presented the highest ecological risk,and there existed high potential ecological risk at some positions with different depths due to high mobility of Cd.Generally,the application of machine learning significantly increased the accuracy of pXRF measurements,and identified key environmental factors.The adapted potential ecological risk assessment emphasized the need to focus on Pb,Cd,and As in future site remediation efforts.展开更多
Objective To compare the performance of five machine learning models and SAPSⅡ score in predicting the 30-day mortality amongst patients with sepsis.Methods The sepsis patient-related data were extracted from the MIM...Objective To compare the performance of five machine learning models and SAPSⅡ score in predicting the 30-day mortality amongst patients with sepsis.Methods The sepsis patient-related data were extracted from the MIMIC-Ⅳ database.Clinical features were generated and selected by mutual information and grid search.Logistic regression,Random forest,LightGBM,XGBoost,and other machine learning models were constructed to predict the mortality probability.Five measurements including accuracy,precision,recall,F1 score,and area under curve(AUC) were acquired for model evaluation.An external validation was implemented to avoid conclusion bias.Results LightGBM outperformed other methods,achieving the highest AUC(0.900),accuracy(0.808),and precision(0.559).All machine learning models performed better than SAPSⅡ score(AUC=0.748).LightGBM achieved 0.883 in AUC in the external data validation.Conclusions The machine learning models are more effective in predicting the 30-day mortality of patients with sepsis than the traditional SAPS Ⅱ score.展开更多
The least squares support vector machine (LS-SVM) is used to study the nonlinear time series prediction. First, the parameter gamma and multi-step prediction capabilities of the LS-SVM network are discussed. Then we e...The least squares support vector machine (LS-SVM) is used to study the nonlinear time series prediction. First, the parameter gamma and multi-step prediction capabilities of the LS-SVM network are discussed. Then we employ clustering method in the model to prune the number of the support values.. The learning rate and the capabilities of filtering noise for LS-SVM are all greatly improved.展开更多
基金financially supported from the National Key Research and Development Program of China(No.2019YFC1803601)the Fundamental Research Funds for the Central Universities of Central South University,China(No.2023ZZTS0801)+1 种基金the Postgraduate Innovative Project of Central South University,China(No.2023XQLH068)the Postgraduate Scientific Research Innovation Project of Hunan Province,China(No.QL20230054)。
文摘A general prediction model for seven heavy metals was established using the heavy metal contents of 207soil samples measured by a portable X-ray fluorescence spectrometer(XRF)and six environmental factors as model correction coefficients.The eXtreme Gradient Boosting(XGBoost)model was used to fit the relationship between the content of heavy metals and environment characteristics to evaluate the soil ecological risk of the smelting site.The results demonstrated that the generalized prediction model developed for Pb,Cd,and As was highly accurate with fitted coefficients(R^(2))values of 0.911,0.950,and 0.835,respectively.Topsoil presented the highest ecological risk,and there existed high potential ecological risk at some positions with different depths due to high mobility of Cd.Generally,the application of machine learning significantly increased the accuracy of pXRF measurements,and identified key environmental factors.The adapted potential ecological risk assessment emphasized the need to focus on Pb,Cd,and As in future site remediation efforts.
文摘Objective To compare the performance of five machine learning models and SAPSⅡ score in predicting the 30-day mortality amongst patients with sepsis.Methods The sepsis patient-related data were extracted from the MIMIC-Ⅳ database.Clinical features were generated and selected by mutual information and grid search.Logistic regression,Random forest,LightGBM,XGBoost,and other machine learning models were constructed to predict the mortality probability.Five measurements including accuracy,precision,recall,F1 score,and area under curve(AUC) were acquired for model evaluation.An external validation was implemented to avoid conclusion bias.Results LightGBM outperformed other methods,achieving the highest AUC(0.900),accuracy(0.808),and precision(0.559).All machine learning models performed better than SAPSⅡ score(AUC=0.748).LightGBM achieved 0.883 in AUC in the external data validation.Conclusions The machine learning models are more effective in predicting the 30-day mortality of patients with sepsis than the traditional SAPS Ⅱ score.
文摘The least squares support vector machine (LS-SVM) is used to study the nonlinear time series prediction. First, the parameter gamma and multi-step prediction capabilities of the LS-SVM network are discussed. Then we employ clustering method in the model to prune the number of the support values.. The learning rate and the capabilities of filtering noise for LS-SVM are all greatly improved.
